Hodson jason (37 resultados)

Paperback. Condición: Brand New. 24 pages. 8.50x8.50x0.06 inches. In Stock. Huffadine, Mr Jason John (ilustrador).

Paperback. Condición: Brand New. 24 pages. 8.50x8.50x0.06 inches. In Stock. Huffadine, Mr Jason John (ilustrador).

Paperback. Condición: Very Good. Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business! In this book, you'll learn about: a. Data PreparationThe first step is to understand your data. Learn about the different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.b. Model Selection Choose the machine learning model that suits your needs! Follow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, clustering, and ensembling.c. Evaluation and IterationAssess and improve the quality of your model! Apply a variety of validation metrics to your model and enhance interpretability to avoid black box code. Then iterate through feature engineering and adding or removing data. d. Implementation and MonitoringYour model is ready to go--now see it in action! Learn how to implement the model to make predictions, monitor its performance, and measure its impact for your business. Highlights include: 1) Real-world use cases2) Data exploration3) Data cleaning4) Model decision framework5) Regression algorithms6) Decision trees7) Clustering8) Validation metrics9) Model iteration 10) Interpretability11) Implementation12) Monitoring.…

Paperback. Condición: Very Good. Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business! In this book, you'll learn about: a. Data PreparationThe first step is to understand your data. Learn about the different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.b. Model Selection Choose the machine learning model that suits your needs! Follow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, clustering, and ensembling.c. Evaluation and IterationAssess and improve the quality of your model! Apply a variety of validation metrics to your model and enhance interpretability to avoid black box code. Then iterate through feature engineering and adding or removing data. d. Implementation and MonitoringYour model is ready to go--now see it in action! Learn how to implement the model to make predictions, monitor its performance, and measure its impact for your business. Highlights include: 1) Real-world use cases2) Data exploration3) Data cleaning4) Model decision framework5) Regression algorithms6) Decision trees7) Clustering8) Validation metrics9) Model iteration 10) Interpretability11) Implementation12) Monitoring.…

Paperback. Condición: new. Paperback. Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business! In this book, you'll learn about: a. Data PreparationThe first step is to understand your data. Learn about the different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.b. Model Selection Choose the machine learning model that suits your needs! Follow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, clustering, and ensembling.c. Evaluation and IterationAssess and improve the quality of your model! Apply a variety of validation metrics to your model and enhance interpretability to avoid black box code. Then iterate through feature engineering and adding or removing data. d. Implementation and MonitoringYour model is ready to go--now see it in action! Learn how to implement the model to make predictions, monitor its performance, and measure its impact for your business. Highlights include: 1) Real-world use cases2) Data exploration3) Data cleaning4) Model decision framework5) Regression algorithms6) Decision trees7) Clustering8) Validation metrics9) Model iteration 10) Interpretability11) Implementation12) Monitoring Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New.

Paperback. Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback. Condición: New.

paperback. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 3-4 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

paperback. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 9-10 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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paperback. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Taschenbuch. Condición: Neu. Neuware -Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business! In this book, you'll learn about:a.Data PreparationThe first step is to understand your data. Learn about the different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.b.Model Selection Choose the machine learning model that suits your needs! Follow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, clustering, and ensembling.c.Evaluation and IterationAssess and improve the quality of your model! Apply a variety of validation metrics to your model and enhance interpretability to avoid black box code. Then iterate through feature engineering and adding or removing data. d.Implementation and MonitoringYour model is ready to go-now see it in action! Learn how to implement the model to make predictions, monitor its performance, and measure its impact for your business. Highlights include: 1)Real-world use cases2)Data exploration3)Data cleaning4)Model decision framework5)Regression algorithms6)Decision trees7)Clustering8)Validation metrics9)Model iteration 10) Interpretability11)Implementation12)Monitoring 440 pp. Englisch.…

Taschenbuch. Condición: Neu. Neuware -Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business! In this book, you'll learn about:a.Data PreparationThe first step is to understand your data. Learn about the different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.b.Model Selection Choose the machine learning model that suits your needs! Follow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, clustering, and ensembling.c.Evaluation and IterationAssess and improve the quality of your model! Apply a variety of validation metrics to your model and enhance interpretability to avoid black box code. Then iterate through feature engineering and adding or removing data. d.Implementation and MonitoringYour model is ready to go-now see it in action! Learn how to implement the model to make predictions, monitor its performance, and measure its impact for your business. Highlights include: 1)Real-world use cases2)Data exploration3)Data cleaning4)Model decision framework5)Regression algorithms6)Decision trees7)Clustering8)Validation metrics9)Model iteration 10) Interpretability11)Implementation12)Monitoring 440 pp. Englisch.…

Taschenbuch. Condición: Neu. Neuware -Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business.…

Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.

Taschenbuch. Condición: Neu. Neuware - Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business! In this book, you'll learn about:a.Data PreparationThe first step is to understand your data. Learn about the different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.b.Model Selection Choose the machine learning model that suits your needs! Follow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, clustering, and ensembling.c.Evaluation and IterationAssess and improve the quality of your model! Apply a variety of validation metrics to your model and enhance interpretability to avoid black box code. Then iterate through feature engineering and adding or removing data. d.Implementation and MonitoringYour model is ready to go-now see it in action! Learn how to implement the model to make predictions, monitor its performance, and measure its impact for your business. Highlights include: 1)Real-world use cases2)Data exploration3)Data cleaning4)Model decision framework5)Regression algorithms6)Decision trees7)Clustering8)Validation metrics9)Model iteration 10) Interpretability11)Implementation12)Monitoring.…

Paperback. Condición: New.

Condición: New.

Condición: New. 2026. paperback. . . . . . Books ship from the US and Ireland.

Condición: New.

Paperback. Condición: Brand New. new edition. 440 pages. 10.00x7.00x1.25 inches. In Stock.

Paperback. Condición: new. Paperback. Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business! In this book, you'll learn about: a. Data PreparationThe first step is to understand your data. Learn about the different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.b. Model Selection Choose the machine learning model that suits your needs! Follow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, clustering, and ensembling.c. Evaluation and IterationAssess and improve the quality of your model! Apply a variety of validation metrics to your model and enhance interpretability to avoid black box code. Then iterate through feature engineering and adding or removing data. d. Implementation and MonitoringYour model is ready to go--now see it in action! Learn how to implement the model to make predictions, monitor its performance, and measure its impact for your business. Highlights include: 1) Real-world use cases2) Data exploration3) Data cleaning4) Model decision framework5) Regression algorithms6) Decision trees7) Clustering8) Validation metrics9) Model iteration 10) Interpretability11) Implementation12) Monitoring Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…